Title: “Amazon dataset vizualization”

Author: Vidya

Output: html_notebook

R version 4.2.2

RStudio 2022.12.0+353 “Elsbeth Geranium” Release (7d165dcfc1b6d300eb247738db2c7076234f6ef0, 2022-12-03) for Windows Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) RStudio/2022.12.0+353 Chrome/102.0.5005.167 Electron/19.1.3 Safari/537.36

Packages: ggplot2,dplyr,plotly Hence, before using these library we need to install these packages install.packages(ggplot2) install.packages(dplyr) install.packages(plotly) install.packages(patchwork)

This analysis will be based on the dataset that contains information of 1465 Amazon products, ratings and reviews, as reported on the company’s official website. https://drive.google.com/drive/folders/1O5ybNrX_kAPskp_gVVlxbvNIE6GtPbxV

Data preprocessing:- As there are lot of categories we need to reduce them into main categories. To acheive this we have used =LEFT(C2, FIND(“|”, C2, FIND(“|”, C2)+1)-1) formula to create a new column called main_category which is used to vizualize the sales data in each category.

---

#importing a ggplot2 library
library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

#reading a csv file from excel

amazon<-read.csv("a2.csv")
#Data preprocesing
amazon$discounted_price <- gsub("[^0-9]", "", amazon$discounted_price)
amazon$actual_price <- gsub("[^0-9]", "", amazon$actual_price)
amazon$rating_count <- gsub("[^0-9]", "", amazon$rating_count)
amazon$discount_percentage <- gsub("[^0-9]", "", amazon$discount_percentage)

#1

#@author Vidya
#@returns A histogram which displays the total number of ratings
#@sample run load variable and execute ggplot snippet

variable <- amazon$rating
ggplot(data.frame(variable), aes(variable)) +
  geom_bar(stat = "count",fill = "cyan4",color = "white") +
  labs(x = "RATING",y = "FREQUENCY",
  title = "RANGE OF RATING FROM 1 TO 5",
  caption = "Bar graph showing the distribution of 'Rating' over the range 1 to 5. People have given 4.1 rating 250 times, followed by 4.2 & 4.3 around 225 times ")+ 
theme(plot.title = element_text(face  = "bold", hjust = 0.5),
  plot.caption = element_text(hjust = 0.5, color = "gray27"),
  panel.grid = element_blank(),
  panel.background = element_rect(fill = "white"),
  axis.text.x = element_text(color = "darkslategray4"),
  axis.text.y = element_text(color = "darkslategray4"))

#1

#@returns A density plot which displays the density of ratings
#@sample run load variable and execute ggplot snippet

#there is NA value in the rating(to avoid repetition used 'rate' instead of 'rating' )
rate<-as.numeric(amazon$rating)

## Warning: NAs introduced by coercion

# checking the datatype of rate
typeof(rate)

## [1] "double"

#replacing the NA value with 0
rate<-replace(rate,is.na(rate),0)
table(is.na(rate))

## 
## FALSE 
##  1465

#Density displaying rating
ggplot(amazon, aes(x=rate)) +
  geom_density()+
  labs(y= "Density",x="Rating",
       title = "HIGHEST DENSITY OF RATING",
       caption = "The Graph Shows Rating as a Density Plot") +
theme(plot.title = element_text(face  = "bold", 
                                hjust = 0.5, 
                                color = "pink3"),
      panel.background = element_rect(fill = "white"),
      panel.grid.minor = element_line(color = "pink3"),
      plot.caption = element_text(color = "pink3", 
                                  hjust = 0.5))

#2
#@returns A pie chart which displays the highest selling product and its share
#@sample run load variable and execute ggplot snippet


# calculate percentages for each main_category
prop_table <-amazon %>% count(main_category) %>%   mutate(props = n / sum(n))
ggplot(prop_table) +
  geom_bar(aes(x = "", y = props, fill = main_category),width = 1,stat = "identity") +
  coord_polar("y", start = 0) +
  labs(x = "",y = "",
  title = "Top selling product categories and their percentage share",
  caption = "Different Product Categories displayed as a Pie Diagram") +
theme(plot.title = element_text(face  = "bold",hjust = -0.1,color = "dodgerblue4"),
  plot.caption = element_text(hjust = 0.5, color = "dodgerblue4"),
  panel.grid = element_blank(),
  panel.background = element_rect(fill = "white"),
  legend.text = element_text(color = "dodgerblue4"),
  legend.position = "right",
  legend.title = element_blank()) +
  guides(fill = guide_legend(title = "main category")) +
  scale_fill_discrete(labels = paste(prop_table$main_category,
  scales::percent(prop_table$props),sep = " - "))

#2
#@returns A bar plot which displays the total product sold in each category 
#@sample run load variable and execute ggplot snippet
#bar chart of category and sales in each category

ggplot(data = amazon) +
  geom_bar(mapping = aes(x = main_category),
           fill = 'steelblue') +
  labs(x = "",y = "",title = "NUMBER OF RATINGS IN EACH CATEGORY",
  caption = "Bar Graph Depicting Total Count of Rating in Each Category") +
theme(plot.title = element_text(face  = "bold",
                                hjust = 0.5,
                                color = "steelblue4"),
  plot.caption = element_text(hjust = 0.5, 
                              color = "steelblue4"),
  axis.text.x = element_text(angle = 45,
                             hjust = 1,
                             color = "steelblue2"),
  axis.text.y = element_text(color = "steelblue2"),
  panel.grid.minor = element_line(color = "pink3"),
  panel.background = element_rect(fill = "white"))

#3
#@returns A point plot which displays discounted percentage and rating for each category
#@sample run load variable and execute ggplot snippet
#enhance the grapgh to full screen to view all percentage
ggplot(data = amazon,mapping = aes(x = discount_percentage , 
                                   y = rating, 
                                   color = main_category)) +
  geom_point() +  
  theme(axis.text.x = element_text(angle = 90,
                                   vjust = 0.5,
                                   hjust = 1)) +
  scale_y_discrete(breaks = seq(0, 5, 0.5)) +
  labs(x = "DISCOUNT PERCENTAGE",y = "RATING",
  title = "DISCOUNT PERCENTAGE INFLUENCING RATING OF EACH PRODUCT CATEGORY",
  caption = "Point Graph Showing the Relationship between Discount Percentage & Each Product category") +
theme(plot.title = element_text(face  = "bold",
                                hjust = 0.5,
                                color = "hotpink4"),
  plot.caption = element_text(hjust = 0.5 , 
                              color = "hotpink4"),
  panel.grid = element_blank(),
  panel.background = element_rect(fill = "white"),
  axis.text.x = element_text(size=5,
                             angle = 90, 
                             color = "steelblue"),
  axis.text.y = element_text(color = "hotpink4"),
  legend.text = element_text(color = "hotpink4"))

#3
#@returns A scatter plot which displays category and its rating
#@sample run load variable and execute ggplot snippet
#enhance the grapgh to full screen to view all percentage
ggplot(data = amazon) +
 geom_bin2d(mapping = aes(x = main_category, 
                          y = rating))+
 labs(x = "", y = "",
 title = "TOTAL COUNT IN PRODUCT CATEGORY",
 caption = "Scatter Plot made with Product Category Against the Rating Count") +
theme(plot.title = element_text(face = "bold", 
                                hjust = 0.5, 
                                color = "steelblue4"),
 plot.caption = element_text(hjust = 0.5, 
                             color = "steelblue4"),
 axis.text.x = element_text(angle = 90, 
                            vjust = 0.5, 
                            hjust=1, 
                            color = "steelblue2"),
 axis.text.y = element_text(color = "steelblue2"),
 panel.background = element_rect(fill = "white"),
 legend.position = "top")

#4
#@returns A bar plot which displays the product category filled with rating
#@sample run load variable and execute ggplot snippet

ggplot(data = amazon) +
  geom_bar(mapping = aes(x = main_category, 
                         fill = rating)) +
  labs(x = "",y = "",
  title = "PRODUCT CATEGORY ACCORDING THEIR RATING COUNT",
  caption = "Bar Chart Displaying Relationship between Product Category & Rating Coungt Filled with Rating") +
theme(axis.text.x = element_text(angle = 45,
                                 hjust = 1,
                                 color = "slateblue1"),
  axis.text.y = element_text(color = "slateblue1"),
  plot.title = element_text(face = "bold",
                            hjust = 0.5,
                            color = "slateblue4"),
  plot.caption = element_text(hjust = 0.5, 
                              color = "slateblue4"),
  legend.text = element_text(color = "slateblue4"),
  panel.grid.major = element_line(color = "seashell2"),
  panel.background = element_rect(fill = "white"))

#4
#@returns A bar plot which displays the max discount percentage in each category
#@sample run load variable and execute ggplot snippet


max_discount <-amazon %>%  group_by(main_category) %>%   summarise(max_discount = max(discount_percentage, na.rm = TRUE))
max_dis<-ggplot(max_discount, mapping = aes(x = main_category, y = max_discount)) +
  geom_bar(stat = "identity",fill = "tan") +
  labs(x = " ",title = "Maximum discount percentage in each product category",
  caption = "Bar Graph of Product Category with Maximum Discount displaying relation between two variables") +
theme(axis.text.x = element_text(angle = 45,vjust = 0.8,hjust = 1),
  plot.title = element_text(face = "bold",hjust = 0.5,color = "tan4"),
  plot.caption = element_text(hjust = 0.5, color = "tan4"),
  axis.text.y = element_text(color = "tan4"),
  panel.grid = element_blank(),
  panel.background = element_rect(fill = "white"))
ggplotly(max_dis)

#5
#@returns A point plot which displays the price between product category on rating
#@sample run load variable and execute ggplot snippet

t <- ggplot(amazon,
            mapping = aes(discounted_price, actual_price,
                          color = main_category)) +
  geom_point()

t + facet_grid(cols = vars(rating),
               rows = vars(main_category)) +
  labs(title = "PRICE AMONGST PRODUCT CATEGORIES REFLECTING ON THE RATING",
       caption = "Difference between Discounted Price & Actual Price with respect to the Product Category and it's Rating") +
  theme(
    plot.title = element_text(
      face = "bold",
      hjust = 0.5,
      color = "thistle4"
    ),
    plot.caption = element_text(
      hjust = 0.5,
      color = "thistle4",
      face = "italic"
    ),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    strip.text = element_text(size = 6),
    legend.position = "bottom"
  )

#6
#@returns A bar interactive plot which displays rating and sales per each category
#@sample run load variable and execute ggplot snippet
#Hover over each bars to display sales counts and rating for each category


vi <- ggplot(amazon, aes(main_category)) +
  geom_bar(aes(fill = rating)) +
  labs(
    x = " ",
    title = "Interactive plot
           displaying the sales figures along with rating for each category",
    caption = " bar graph interactive"
  ) +
  theme(
    axis.text.x = element_text(angle = 45,  hjust = 1),
    plot.title = element_text(
      face = "bold",
      hjust = 0.5,
      color = "tan4"
    ),
    plot.caption = element_text(hjust = 0.5, color = "tan4"),
    axis.text.y = element_text(color = "tan4"),
    panel.grid = element_blank(),
    panel.background = element_rect(fill = "white")
  )

ggplotly(vi)

REFERENCES:- [1]“Data Mentor,” DataMentor, Nov. 23, 2017. https://www.datamentor.io/r-programming/box-plot/ [2]“Colors in R.” Available: http://www.stat.columbia.edu/~tzheng/files/Rcolor.pdf [3]“ggplot2 package - RDocumentation,” www.rdocumentation.org. https://www.rdocumentation.org/packages/ggplot2/versions/3.4.1 [4]“Package ‘ggplot2’ Title Create Elegant Data Visualisations Using the Grammar of Graphics,” 2020. Available: https://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf [5] A. Misiuro, “What is Data Visualization | Top Data Visualization Tools,” Synder blog, Jun. 17, 2022. https://synder.com/blog/what-is-data-visualization/